For a decade swarm Intelligence is concerned with the design of intelligent systems by taking inspiration from the collective behaviors of social insects. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the procedure of most successful methods of optimization techniques inspired by Swarm Intelligence: Ant Colony Optimization (ACO) and P...

PSO algorithm is an intelligent optimization algorithm based on swarm intelligence. Particle swarm optimization algorithm is simple, easy to implement, and it has a wide application prospect in scientific research and engineering applications. In real life, most of the optimization problem is the optimization problem of some nonlinear discrete with the existence of local. PSO algorithm also has...

Reader network planning (RNP) problem of radio frequency identification (RFID) system is a combinational optimization problem. In this study, we propose a genetic algorithm (GA) to solve this RNP problem. We have tested the proposed GA on several RNP problems and compare with a particle swarm optimization (PSO) method by solving the same RNPs. The comparison results demonstrate that the propose...

This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multiobjective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimizatio...

The Particle Swarm Optimization is very efficient in intrusion detection in the networks. However, many intrusion detection systems either fail to detect or falsely detect the intrusions. This paper proposes a technique for intrusion detection using Particle Swarm Optimization with Genetic Algorithm based feature selection and using Adaptive Mutation for slow convergence of optimization algorit...

Stochastic optimization algorithms are usually evaluated based on performance on high dimensional benchmark functions and results of these tests determine the direction of development. Benchmark functions however, do not emulate complex engineering problems. In this paper a power plant optimization problem is presented and solved under different constraints with multiple elite dependent and sin...

Economic dispatch (ED) problem is a nonlinear and non-smooth optimization problem when valvepoint effects, multi-fuel effects and prohibited operating zones (POZs) have been considered. This paper presents an efficient evolutionary method for a constrained ED problem using the new adaptive particle swarm optimization (NAPSO) algorithm. The original PSO has difficulties in premature convergence,...

This paper presents a method to employ particle swarm optimization in a split architecture injected with a plain ‘attractor’ configuration. This is achieved by splitting the input vector into two even sub-vectors, each of which is optimized in its own swarm. Then, a plain ‘attractor’ is injected into each swarm. The application of this technique to neural network training is investigated. Key-W...

This paper presents a Particle Swarm Optimization with Improved Inertia Wight (PSO-IIW) for Combined Heat and Power Economic Dispatch (CHPED) problem. The proposed PSO-IIW technique, which is a population based global search and optimization technique, has been developed to solve the CHPED problem. The CHPED problem is formulated as an optimization problem which is solved by the PSO-IIW techniq...

Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of adjacent particles. PSO algorithms are m...